Unlocking Insights: Analysing COVID-19 Lockdown Policies and Mobility Data in Victoria, Australia, through a Data-Driven Machine Learning Approach

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The study is significant, however the following should be added 

1. Literature review

2. Research design 

3. Policy and practical implication of your research

The researchers should improve the conclusion and add recommendation.

Author Response

see the attached file

Author Response File: Author Response.docx

Reviewer 2 Report

Comments and Suggestions for Authors

The paper presents a great work. I just wish you could provide the code and processed data available for downloads. 

Author Response

see the attached file

Author Response File: Author Response.docx

Reviewer 3 Report

Comments and Suggestions for Authors

This article evaluates the effectiveness of short-term and long-term social restrictions during the extended COVID-19 lockdowns in Victoria, Australia in 2020 and 2021. However, several concerns arise:

1.       When the authors refer to “social restriction policies”, I expect to see some more detailed policies such as mask mandate, limited dine in, and so on. I then realized that the authors exclusively refer to long- and short-term lockdowns. The authors should clarify this early in the paper.

2.       The description of the machine learning models is not clear. Based on the current presentation, I am not sure what are the explanatory and dependent variables in the models. The authors should provide more detailed information on the models used.

3.       The paper could benefit from a discussion on how this research can inform future epidemiological studies and guide their direction.

Author Response

see the attached file

Author Response File: Author Response.docx

Reviewer 4 Report

Comments and Suggestions for Authors

Thank you for submitting your paper titled "Unlocking Insights: Analyzing COVID-19 Lockdown Policies and Mobility Data in Victoria, Australia through Data-Driven Machine Learning approach". During the review process, I found that your research helps us better understand the relationship between COVID-19 lockdown policies and mobility, and has practical value for epidemic control and management. However, I also found some issues and areas for improvement while reading. Below are my suggestions and comments on your paper, please refer to and revise accordingly:

(1) The abstract section lacks a detailed introduction to the research background, such as why Victoria, Australia was chosen as the research object, and how the lockdown situation is in other countries or regions? It is suggested that at the beginning of the abstract, you can briefly introduce the background of the COVID-19 epidemic, as well as the lockdown measures and effects adopted by countries around the world.

(2) The abstract mentions that "increased mobility data indicates decreased compliance", it would be helpful to briefly explain the process of reaching this conclusion, as well as the results compared with other studies, to enhance the persuasiveness of the research.

(3) In describing the Min-Max scaling method on line 125 of the paper, you can provide more background information. For example, you can explain why it is necessary to standardize the number of confirmed cases and vaccinations, as well as the specific steps of the Min-Max feature scaling method. In addition, you can explain the meaning of the obtained standardized values, and why these values range from 0 to 10.

(4) In the Exploratory data analysis section (3.1), when describing long-term trends, it is suggested to use clearer vocabulary to describe changes in R values, such as "gradually increasing" or "steadily rebounding", to avoid ambiguity.

(5) On line 357 of the paper, when mentioning the increased risk of student obesity and screen addiction, it is recommended to cite relevant research or data sources to enhance the credibility of the paper.

(6) At the end of the paper, when referring to "significant decreases in compliance", it could be further clarified which restrictions showed decreased compliance, such as social distancing or stay-at-home orders. It could also be further explained which types of mobility measures increased, such as travel frequency or transportation usage rate.

Author Response

see the attached file

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

The literature review needs improvement.  

The contribution of the research in term policy and practice should be elaborated. 

Author Response

Please see attachment

Author Response File: Author Response.docx

Reviewer 3 Report

Comments and Suggestions for Authors

The authors have addressed all my questions.

Author Response

Thank you for your positive review and acceptance of our manuscript in its current form. We are grateful for the opportunity to contribute to the field and look forward to seeing our work published.

Reviewer 4 Report

Comments and Suggestions for Authors

Accept in present form.

Author Response

Thank you for your positive review and acceptance of our manuscript in its current form. We are grateful for the opportunity to contribute to the field and look forward to seeing our work published.